Stability Analysis of Equilibrium Point and Limit Cycle of Two-Dimensional Nonlinear Dynamical Systems—A Tutorial

The equilibrium state of a dynamical system can be divided into the equilibrium point and limit cycle. In this paper, the stability analysis of the equilibrium point and limit cycle of dynamical systems are presented through different and all possible approaches, and those approaches are compared as well. In particular, the author presented the stability analysis of the equilibrium point through phase plane approach, Lyapunov–LaSalle energy-based approach, and linearization approach, respectively, for two-dimensional nonlinear system, while the stability analysis of the limit cycle is analyzed by using the LaSalle local invariant set theorem and Poincaré–Bendixson theorem, which is only valid in two-dimensional systems. Different case studies are used to demonstrate the stability analysis of equilibrium point and limit cycle.

Stepsize Range and Optimal Value for Taylor-Zhang Discretization Formula Applied to Zeroing Neurodynamics Illustrated via Future Equality-Constrained Quadratic Programming

In this brief, future equality-constrained quadratic programming (FECQP) is studied. Via a zeroing neurodynamics method, a continuous-time zeroing neurodynamics (CTZN) model is presented. By using Taylor-Zhang discretization formula to discretize the CTZN model, a Taylor-Zhang discrete-time zeroing neurodynamics (TZ-DTZN) model is presented to perform FECQP. Furthermore, we focus on the critical parameter of the TZ-DTZN model, i.e., stepsize. By theoretical analyses, we obtain an effective range of the stepsize, which guarantees the stability of the TZ-DTZN model. In addition, we further discuss the optimal value of the stepsize, which makes the TZ-DTZN model possess the optimal stability (i.e., the best stability with the fastest convergence). Finally, numerical experiments and application experiments for motion generation of a robot manipulator are conducted to verify the high precision of the TZ-DTZN model and the effective range and optimal value of the stepsize for FECQP.

Multistability and instability analysis of recurrent neural networks with time-varying delays

This paper provides new theoretical results on the multistability and instability analysis of recurrent neural networks with time-varying delays. It is shown that such n-neuronal recurrent neural networks have exactly [Formula: see text] equilibria, [Formula: see text] of which are locally exponentially stable and the others are unstable, where k₀ is a nonnegative integer such that k₀≤n. By using the combination method of two different divisions, recurrent neural networks can possess more dynamic properties. This method improves and extends the existing results in the literature. Finally, one numerical example is provided to show the superiority and effectiveness of the presented results.

Global exponential stability of cellular neural networks with multi-proportional delays

In this paper, a class of cellular neural networks (CNNs) with multi-proportional delays is studied. The nonlinear transformation yi(t) = xi( e t) transforms a class of CNNs with multi-proportional delays into a class of CNNs with multi-constant delays and time-varying coefficients. By applying Brouwer fixed point theorem and constructing the delay differential inequality, several delay-independent and delay-dependent sufficient conditions are derived for ensuring the existence, uniqueness and global exponential stability of equilibrium of the system and the exponentially convergent rate is estimated. And several examples and their simulations are given to illustrate the effectiveness of obtained results.

Independent research of China in Science Citation Index Expanded during 1980-2011

The study explores the characteristics of China's independent research articles published from 1980 to 2011, based on the database of Science Citation Index Expanded. The publication outputs of seven major industrialized countries including Canada, France, Japan, Germany, Italy, the UK, and the USA were compared with China. Annual production, field performance, research emphases and trends, top articles, as well as main institutional and individual contributors by its top cited articles were analyzed. Some newly developed indicators related to words in title, author keywords, KeyWords Plus, first author, corresponding author, and Y-index were employed to provide in-depth information on topic and author contributions. Results showed that China has been closing the gap with the USA with the greatest growth, and has stood the second since 2006. Most top cited articles were published in 2000s, made up approximately seven tenths of total articles. Pronounced activities were found in chemistry and physics related categories. The core categories included multidisciplinary chemistry, physical chemistry, multidisciplinary materials science, and applied physics. Moreover, China's performance of nanotechnology and science, especially carbon nanotubes, nanoparticles, nanowires, and nanostructures showed dramatic growth. Six top articles with at least 1000 citations were examined, and were observed to concern medicine, nanotube, and adsorption. In addition, main contributing institutions and authors were also revealed and evaluated. Chinese Academy of Sciences played a dominant role, and Tsinghua University, Peking University and five universities in Hong Kong showed good scientific performance.

Multistability of neural networks with Mexican-hat-type activation functions

In this paper, we are concerned with a class of neural networks with Mexican-hat-type activation functions. Due to the different structure from neural networks with saturated activation functions, a set of new sufficient conditions are presented to study the multistability, including the total number of equilibrium points, their locations, and stability. Furthermore, the attraction basins of stable equilibrium points are investigated for two-neuron neural networks. The investigation shows that the stable manifolds of unstable equilibrium points constitute the boundaries of attraction basins of stable equilibrium points. Several illustrative examples are given to verify the effectiveness of our results.

Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects

This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.

Robustness analysis of global exponential stability of recurrent neural networks in the presence of time delays and random disturbances

In recent years, the global stability of recurrent neural networks (RNNs) has been investigated extensively. It is well known that time delays and external disturbances can derail the stability of RNNs. In this paper, we analyze the robustness of global stability of RNNs subject to time delays and random disturbances. Given a globally exponentially stable neural network, the problem to be addressed here is how much time delay and noise the RNN can withstand to be globally exponentially stable in the presence of delay and noise. The upper bounds of the time delay and noise intensity are characterized by using transcendental equations for the RNNs to sustain global exponential stability. Moreover, we prove theoretically that, for any globally exponentially stable RNNs, if additive noises and time delays are smaller than the derived lower bounds arrived at here, then the perturbed RNNs are guaranteed to also be globally exponentially stable. Three numerical examples are provided to substantiate the theoretical results.

Convergence dynamics of stochastic Cohen-Grossberg neural networks with unbounded distributed delays

This paper addresses the issue of the convergence dynamics of stochastic Cohen-Grossberg neural networks (SCGNNs) with white noise, whose state variables are described by stochastic nonlinear integro-differential equations. With the help of Lyapunov functional, semi-martingale theory, and inequality techniques, some novel sufficient conditions on pth moment exponential stability and almost sure exponential stability for SCGNN are given. Furthermore, as byproducts of our main results, some sufficient conditions for checking stability of deterministic CGNNs with unbounded distributed delays have been established. Especially, even when the spectral radius of the coefficient matrix is greater than 1, in some cases our theory is also effective.